Skinly: A novel handheld IoT device for validating biophysical skin characteristics.

BACKGROUND: Recent advancements in artificial intelligence have revolutionized dermatological diagnostics. These technologies, particularly machine learning (ML), including deep learning (DL), have shown accuracy equivalent or even superior to human experts in diagnosing skin conditions like melanoma. With the integration of ML, including DL, the development of at home skin analysis devices has become feasible. To this end, we introduced the Skinly system, a handheld device capable of evaluating various personal skin characteristics noninvasively. MATERIALS AND METHODS: Equipped with a moisture sensor and a multi-light-source camera, Skinly can assess age-related skin parameters and specific skin properties. Utilizing state-of-the-art DL, Skinly processed vast amounts of images efficiently. The Skinly system's efficacy was validated both in the lab and at home, comparing its results to established "gold standard" methods. RESULTS: Our findings revealed that the Skinly device can accurately measure age-associated parameters, that is, facial age, skin evenness, and wrinkles. Furthermore, Skinly produced data consistent with established devices for parameters like glossiness, skin tone, redness, and porphyrin levels. A separate study was conducted to evaluate the effects of two moisturizing formulations on skin hydration in laboratory studies with standard instrumentation and at home with Skinly. CONCLUSION: Thanks to its capability for multi-parameter measurements, the Skinly device, combined with its smartphone application, holds the potential to replace more expensive, time-consuming diagnostic tools. Collectively, the Skinly device opens new avenues in dermatological research, offering a reliable, versatile tool for comprehensive skin analysis.

Depth-dependent hydration dynamics in human skin: Vehicle-controlled efficacy assessment of a functional 10% urea plus NMF moisturizer by near-infrared confocal spectroscopic imaging (KOSIM IR) and capacitance method complemented by volunteer perception.

BACKGROUND: Stratum corneum (SC) hydration is vital for the optimal maintenance and appearance of healthy skin. In this context, we evaluated the efficacy of an NMF-enriched moisturizer containing 10% urea on different aspects of SC hydration of dry skin. MATERIAL AND METHODS: In two clinical studies, the hydration efficacy of the moisturizer in comparison to its vehicle was investigated. In the first study, 42 subjects applied the moisturizer and the vehicle to one lower leg each. Thirty minutes and 24 h after this single treatment, SC hydration was measured by corneometry. Volunteers also rated skin moisturization and evaluated product properties. In the second study, 27 subjects each treated one forearm twice daily for 2 weeks with the moisturizer and the vehicle. Then, depth-resolved water-absorption spectra were measured by near-infrared confocal spectroscopic imaging (KOSIM IR). RESULTS: The moisturizer exerted a superior hydrating effect compared to the vehicle. KOSIM IR measurements show that, compared to the vehicle, the moisturizer significantly improved the water gradient in the SC from the surface to a depth of 15 µm. Moreover, the moisturizer received high acceptance ratings from the volunteers and was preferred to the vehicle. CONCLUSION: The humectants applied in the investigated moisturizer improved SC water content in total and as a function of depth. The combination of depth-resolved data (KOSIM IR) with classical corneometry provides an integrated concept in the measurement of skin hydration, rendering both methods complementary. These findings were in line with the volunteers` self-assessments of the moisturizer properties that are relevant to treatment adherence.